Functional Programming Patterns: A Cookbook

This article targets an audience that’s graduating from functional libraries like ramda to using Algebraic Data Types. We’re using the excellent crocks library for our ADTs and helpers, although these concepts may apply to other ones as well. We’ll be focusing on demonstrating practical applications and patterns without delving into a lot of theory.

Safely Executing Dangerous Functions

Let’s say we have a situation where we want to use a function called darken from a third-party library. darken takes a multiplier, a color and returns a darker shade of that color.

tryCatch executes the provided function within a try-catch block and returns a Sum Type called Result. In its essence, a Sum Type is basically an “or” type. This means that the Result could be either an Ok if an operation is successful or an Error in case of failures. Other examples of Sum Types include Maybe, Either, Async and so on. The either point-free helper breaks the value out of the Result box, and returns the CSS default inherit if things went south or the darkened color if everything went well.

Enforcing Types using Maybe Helpers

With JavaScript, we often run into cases where our functions explode because we’re expecting a particular data type, but we receive a different one instead. crocks provides the safe, safeAfter and safeLift functions that allow us to execute code more predictably by using the Maybe type. Let’s look at a way to convert camelCased strings into Title Case.

We’ve created a helper function match that uses safeAfter to iron out String.prototype.match’s behavior of returning an undefined in case there are no matches. The isArray predicate ensures that we receive a Nothing if there are no matches found, and a Just [String] in case of matches. safeAfter is great for executing existing or third-party functions in a reliable safe manner.

(Tip: safeAfter works really well with ramda functions that return a | undefined.)

Our uncamelize 🐪 function is executed with safeLift(isString) which means that it’ll only execute when the input returns true for the isStringpredicate.

In addition to this, crocks also provides the prop and propPath helpers which allow you to pick properties from Objects and Arrays.

This is great, especially if we’re dealing with data from side-effects that are not under our control like API responses. But what happens if the API developers suddenly decide to handle formatting at their end?

Runtime errors! We tried to invoke the toFixed method on a String, which doesn’t really exist. We need to make sure that bankBalance is really a Number before we invoke toFixed on it. Let’s try to solve it with our safe helper.

We pipe the results of the prop function to our safe(isNumber) function which also returns a Maybe, depending on whether the result of propsatisfies the predicate. The pipeline above guarantees that the last mapwhich contains the toFixed will only be called when bankBalance is a Number.

If you’re going to be dealing with a lot of similar cases, it would make sense to extract this pattern as a helper:

Using Applicatives to keep Functions Clean

Often times, we find ourselves in situations where we would want to use an existing function with values wrapped in a container. Let’s try to design a safe add function that allows only numbers, using the concepts from the previous section. Here’s our first attempt.

This does exactly what we need, but our add function is no longer a simple a + b. It has to first lift our values into Maybes, then reach into them to access the values, and then return the result. We need to find a way to preserve the core functionality of our add function while allowing it to work with values contained in ADTs! Here’s where Applicative Functors come in handy.

An Applicative Functor is just a like a regular functor, but along with map, it also implements two additional methods:

of :: Applicative f => a -> f a

The of is a completely dumb constructor, and lifts any value that you give it into our data type. It’s also referred to as pure in other languages.

Maybe.of(null)
//=> Just null
Const.of(42)
//=> Const 42

And here’s where all the money is — the ap method:

ap :: Apply f => f a ~> f (a -> b) -> f b

The signature looks very similar to map, with the only difference being that our a -> b function is also wrapped in an f. Let’s see this in action.

We first lift our curried add function into a Maybe, and then apply Maybe aand Maybe b to it. We’ve been using map so far to access the value inside a container and ap is no different. Internally, it maps on safeNumber(a) to access the a and applies it to add. This results in a Maybe that contains a partially applied add. We repeat the same process with safeNumber(b) to execute our add function, resulting in a Just of the result if both a and bare valid or a Nothing otherwise.

Crocks also provides us the liftA2 and liftN helpers to express the same concept in a pointfree manner. A trivial example follows:

liftA2(add)(Maybe(1))(Maybe(2))
//=> Just 3

We shall use this helper extensively in the section Expressing Parallelism.

Tip: Since we’ve observed that ap uses map to access values, we can do cool things like generating a Cartesian product when given two lists.

Using Async for Predictable Error Handling

crocks provides the Async data type that allows us to build lazy asynchronous computations. To know more about it, you can refer to the extensive official documentation here. This section aims to provide examples of how we can use Async to improve the quality of our error reporting and make our code resilient.

Often, we run into cases where we want to make API calls that depend on each other. Here, the getUser endpoint returns a user entity from GitHub and the response contains a lot of embedded URLs for repositories, stars, favorites and so on. We will see how we can design this use case with using Async.

The usage of the maybeToAsync transformation allows us to use all of the safety features that we get from using Maybe and bring them to our Asyncflows. We can now flag input and other errors as a part of our Async flows.

Using Monoids Effectively

We’ve already been using Monoids when we perform operations like String/Array concatenation and number addition in native JavaScript. It’s simply a data type that offers us the following methods.

concat :: Monoid m => m a -> m a -> m a

concat allows us to combine two Monoids of the same type together with a pre-specified operation.

empty :: Monoid m => () => m a

The empty method provides us with an identity element, that when concat ed with other Monoids of the same type, would return the same element. Here’s what I’m talking about.

The mconcat and mreduce methods take a Monoid and a list of elements to work with, and apply concat to all of their elements. The only difference between them is that mconcat returns an instance of the Monoid while mreduce returns the raw value. The mconcatMap and mreduceMap helpers work in the same way, except that they accept an additional function that is used to map over every element before calling concat.

Let’s look at another example of a Monoid from crocks, the First Monoid. When concatenating, First will always return the first, non-empty value.

Expressing Parallelism in a Pointfree manner

We might run into cases where want to perform multiple operations on a single piece of data and combine the results in some way. crocks provides us with two methods to achieve this. The first pattern leverages Product Types Pair and Tuple. Let’s look at a small example where we have an object that looks like this:

{ ids: [11233, 12351, 16312], rejections: [11233] }

We would like to write a function that accepts this object and returns an Array of ids excluding the rejected ones. Our first attempt in native JavaScript would look like this:

This of course works, but it would explode in case one of the properties is malformed or is not defined. Let’s make getIds return a Maybe instead. We use fanout helper that accepts two functions, runs it on the same input and returns a Pair of the results.

One of the main benefits of using the pointfree approach is that it encourages us to break our logic into smaller pieces. We now have the reusable helper difference (with liftA2, as seen previously) that we can use to merge both halves the Pair together.

The second method would be to use the converge combinator to achieve similar results. converge takes three functions and an input value. It then applies the input to the second and third function and pipes the results of both into the first. Let’s use it to create a function that normalizes an Arrayof objects based on their ids. We will use the Assign Monoid that allows us to combine objects together.

Using Traverse and Sequence to Ensure Data Sanity

We’ve seen how to use Maybe and friends to ensure that we’re always working with the types we expect. But what happens when we’re working with a type that contains other values, like an Array or a List for example? Let’s look at a simple function that gives us the total length of all strings contained within an Array.

Not really. Our function doesn’t guarantee that the contents of the list won’t hold any surprises. One of the ways we could solve this would be to define a safeLength function that only works with strings:

sequence helps swap the inner type with the outer type while performing a certain effect, given that the inner type is an Applicative. The sequence on Identity is pretty dumb — it just maps over the inner type and returns the contents wrapped in an Identity container. For List and Array, sequenceuses reduce on the list to combine its contents using ap and concat. Let’s see this in action in our refactored totalLength implementation.

Great! We’ve built a completely bulletproof totalLength. This pattern of mapping over something from a -> m b and then using sequence is so common that we have another helper called traverse which performs both operations together. Let’s see how we can use traverse instead of sequence in the above example.

There! It works exactly the same way. If we think about it, our sequenceoperator is basically traverse, with an identity as the mapping function.

Note: Since we cannot infer inner type using JavaScript, we have to explicitly provide the type constructor as the first argument to traverse and sequence.

It’s easy to see how sequence and traverse are invaluable for validating data. Let’s try to create a generic validator that takes a schema and validates an input object. We’ll use the Result type, which accepts a Semigroup on the left side that allows us to collect errors. A Semigroup is similar to a Monoid and it defines a concat method — but unlike the Monoid, it doesn’t require the presence of the empty method. We’re also introducing the transformation function maybeToResult below, that’ll help us interoperate between Maybe and Result.

Since we’ve flipped the makeValidator function to make more suitable for currying, our compose chain receives the schema that we need to validate against first. We first break the schema into key-value Pairs, and pass the value of each property to it’s corresponding validation function. In case the function fails, we use bimap to map on the error, add some more information to it, and return it as a singleton Array. traverse will then concat all the errors if they exist, or return the original object if it’s valid. We could have also returned a String instead of an Array, but an Arrayfeels much nicer.

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